Recent interests have shifted to large-factor SR beyond the typical upscaling factors (\(2{\times }\)  or \(4{\times }\) ) [1]}, [2]}, [3]}, [4]}.
Dahl  [1]} propose a fully probabilistic pixel recursive network for upsampling extremely coarse images with resolution \(8{\times }8\) .
RFB-ESRGAN [3]} builds upon ESRGAN and adopts multi-scale receptive fields blocks for \(16{\times }\)  SR.
VarSR [2]} achieves \(8{\times }\)  SR by matching the latent distributions of LR and HR images to recover the missing details.
Zhang  [4]} perform \(16{\times }\)  reference-based SR on paintings with a non-local matching module and a wavelet texture loss.
To handle even larger magnification factors, one would need to rely on stronger priors. SR methods specialized on large magnification factors are typically dedicated to the human face category as one could exploit the strong structural prior of faces.
Facial priors including facial attributes [9]}, facial landmarks [10]}, [11]}, and identity [12]} have been studied.
Our work goes beyond previous works and pushes the limit to \(64{\times }\)  and generalizes to more categories. Such a large magnification factor is challenging due to its highly ill-posed nature.
